It is difficult to arrive at an efficient and widely acceptable set of common data elements (CDEs). Trial outcomes, as defined in a clinical trial registry, offer a large set of elements to analyze. However, all clinical trial outcomes is an overwhelming amount of information. One way to reduce this amount of data to a usable volume is to only use a subset of trials. Our method uses a subset of trials by considering trials that support drug approval (pivotal trials) by Food and Drug Administration. We identified a set of pivotal trials from FDA drug approval documents and used primary outcomes data for these trials to identify a set of important CDEs. We identified 76 CDEs out of a set of 172 data elements from 192 pivotal trials for 100 drugs. This set of CDEs, grouped by medical condition, can be considered as containing the most significant data elements.